Zhongnan Zhang

A K-means clustering with optimized initial center based on Hadoop platform

With the explosive growth of data, the traditional clustering algorithms running on separate servers can not meet the demand. To solve the problem, more and more researchers implement the traditional clustering algorithms on the cloud computing platforms, especially for K-means clustering. But, few researchers pay attention to the K-means clustering structure, and most of researchers optimized the model of the cloud computing platform to raise the computing speed of K-means clustering. However the problem of instability caused by the random initial centers still exists. In this paper, we propose a K-means clustering algorithm with optimized initial centers based on data dimensional density. This method avoids the deficiency of the random initial centers and improves the stability of the K-means clustering. The experimental results show that the approach achieves a good performance on K-means, and improves the accuracy of K-means clustering on the test set.

Multi-Objective Algorithm for Blood Supply via Unmanned Aerial Vehicles to the Wounded in an Emergency Situation

Unmanned aerial vehicle (UAV) has been widely used in many industries. In the medical environment, especially in some emergency situations, UAVs play an important role such as the supply of medicines and blood with speed and efficiency. In this paper, we study the problem of multi-objective blood supply by UAVs in such emergency situations. This is a complex problem that includes maintenance of the supply blood’s temperature model during transportation, the UAVs’ scheduling and routes’ planning in case of multiple sites requesting blood, and limited carrying capacity. Most importantly, we need to study the blood’s temperature change due to the external environment, the heating agent (or refrigerant) and time factor during transportation, and propose an optimal method for calculating the mixing proportion of blood and appendage in different circumstances and delivery conditions. Then, by introducing the idea of transportation appendage into the traditional Capacitated Vehicle Routing Problem (CVRP), this new problem is proposed according to the factors of distance and weight. Algorithmically, we use the combination of decomposition-based multi-objective evolutionary algorithm and local search method to perform a series of experiments on the CVRP public dataset. By comparing our technique with the traditional ones, our algorithm can obtain better optimization results and time performance.

Adaptive location recommendation algorithm based on location-based social networks

With the development of social network and location-based services, location-based social network rose. In the Geo-Social recommended system, location recommendation has become a focus of recent research. This paper analyzes three questions the personalized recommendation algorithm may face: location data sparseness, cold start and registered locations near and far from the usual residence. Through the analysis of those questions, we propose an improved adaptive location recommendation algorithm. This algorithm merges user collaborative filtering, social influence, and naive Bayesian classification. It adapts to the users current location, and recommend the most suitable location. In this paper, we compare the improved algorithm with other recommendation algorithms, verifying the feasibility, and effectiveness of the improved algorithm. Experimental results indicate that the improved algorithm can solve the problems of personalized place recommendations, and recommend place better.

A Parallel Bee Colony Algorithm for Resource Allocation Application in Cloud Computing Environment

Cloud computing has been widely used in every social field. The problem of energy consumption in a cloud computing environment has brought cost pressure to service providers and affected the natural environment. However, the reasonable and efficient scheduling of resources could save a lot of energy for cluster. Meanwhile, its necessary for us to take into account of the emergent needs of every consumer. So the resource scheduling is often regarded as a multi-objective problem with the optimization of energy consumption and time cost. We redefine the problem in this paper and set up a multi-objective optimization model, and the parallel computing is improved on the basis of bee colony algorithm. Furthermore, multi-objective problem optimization based on fast non-dominated sorting method is used in parallel environment. Experimental results show that the proposed algorithm can save energy, reduce the execution time of tasks and have very good stability in parallel environment.

Features extraction and multi-classification of sEMG using a GPU-Accelerated GA/MLP hybrid algorithm

BACKGROUND Surface electromyography (sEMG) signal is the combined effect of superficial muscle EMG and neural electrical activity. In recent years, researchers did large amount of human-machine system studies by using the physiological signals as control signals. OBJECTIVE To develop and test a new multi-classification method to improve performance of analyzing sEMG signals based on public sEMG dataset. METHODS First, ten features were selected as candidate features. Second, a genetic algorithm (GA) was applied to select representative features from the initial ten candidates. Third, a multi-layer perceptron (MLP) classifier was trained by the selected optimal features. Last, the trained classifier was used to predict the classes of sEMG signals. A special graphics processing unit (GPU) was used to speed up the learning process. RESULTS Experimental results show that the classification accuracy of the new method reached higher than 90%. Comparing to other previously reported results, using the new method yielded higher performance. CONCLUSIONS The proposed features selection method is effective and the classification result is accurate. In addition, our method could have practical application value in medical prosthetics and the potential to improve robustness of myoelectric pattern recognition.

Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing

BACKGROUND Epilepsy is a chronic disease with transient brain dysfunction that results from the sudden abnormal discharge of neurons in the brain. Since electroencephalogram (EEG) is a harmless and noninvasive detection method, it plays an important role in the detection of neurological diseases. However, the process of analyzing EEG to detect neurological diseases is often difficult because the brain electrical signals are random, non-stationary and nonlinear. OBJECTIVE In order to overcome such difficulty, this study aims to develop a new computer-aided scheme for automatic epileptic seizure detection in EEGs based on multi-fractal detrended fluctuation analysis (MF-DFA) and support vector machine (SVM). METHODS New scheme first extracts features from EEG by MF-DFA during the first stage. Then, the scheme applies a genetic algorithm (GA) to calculate parameters used in SVM and classify the training data according to the selected features using SVM. Finally, the trained SVM classifier is exploited to detect neurological diseases. The algorithm utilizes MLlib from library of SPARK and runs on cloud platform. RESULTS Applying to a public dataset for experiment, the study results show that the new feature extraction method and scheme can detect signals with less features and the accuracy of the classification reached up to 99%. CONCLUSIONS MF-DFA is a promising approach to extract features for analyzing EEG, because of its simple algorithm procedure and less parameters. The features obtained by MF-DFA can represent samples as well as traditional wavelet transform and Lyapunov exponents. GA can always find useful parameters for SVM with enough execution time. The results illustrate that the classification model can achieve comparable accuracy, which means that it is effective in epileptic seizure detection.

WITHDRAWN: Automatic epileptic seizure detection in EEGs based on MF-DFA and SVM

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.

Research on statistics-based model for E-commerce user purchase prediction

This paper describes our work for ALIDATA DISCOVERY competition. Through analyzing massive real-world user action data provided by Tmall, one of the largest B2C online retail platforms in China, we try to predict future user purchases. The prediction results are judged by F1 Score that is consist of two parts, precision and recall rate. The provided data set contains more than 500 million action records from over 12 million distinct users. Such a massive data set drives us to finish the task in MapReduce fashion on the Open Data Processing Service (ODPS) platform. According to statistical results, we classify all users into different groups firstly. Then the rule model, timing model, statistics model are adopted for predicting future user purchases. By comparison, the statistics model obtains the best F1Score.

DTI-RCNN: New Efficient Hybrid Neural Network Model to Predict Drug–Target Interactions

Drug-target interactions (DTIs) are a critical step in the technology of new drugs discovery and drug repositioning. Various computational algorithms have been developed to discover new DTIs, whereas the prediction accuracy is not very satisfactory. Most existing computational methods are based on homogeneous networks or on integrating multiple data sources, without considering the feature associations between gene and drug data. In this paper, we proposed a deep-learning-based hybrid model, DTI-RCNN, which integrates long short term memory (LSTM) networks with convolutional neural network (CNN) to further improve DTIs prediction accuracy using the drug data and gene data. First, we extracted potential semantic information between gene data and drug data via a LSTM network. We then constructed a CNN to extract the loci knowledge in the LSTM outputs. Finally, a fully connected network was used for prediction. The results comparison shows that the proposed model exhibits better performance. More importantly, DTI-RCNN is stable and efficient in predicting novel DTIs. Therefore, it should help select candidate DTIs, and further promote the development of drug repositioning.

Deep learning-based transcriptome data classification for drug-target interaction prediction

BackgroundThe ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity.ResultsIn this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions.ConclusionsOur model’s capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process.